Why simulation is the first gate before any human contact
Simulation lets you test thousands of grasps without risking a person or a robot. DexGraspNet generated 1.32 million grasps for 5,355 objects and validated every one in the Isaac Gym simulator—that scale is only possible in a virtual environment [2]. The point isn't just volume; it's that simulation catches failures cheaply and lets you iterate on the grasp synthesis algorithm before hardware is involved.
Simulation also serves as a training ground for real-world transfer. SKT-Hang, which plans trajectories for hanging objects, was trained entirely in simulation and then showed promising results on a real robot [5]. That pattern—simulate first, then test on hardware—is the standard evidence-backed approach for object-agnostic planning.
What to check in real-world tests before people are nearby
Once simulation passes, move to real-world tests with a human in the loop but with explicit safety criteria. Fast-EgoHOS, a framework for human-to-robot handovers, selects grasps based on criteria like hand-grasp distance to keep the robot's end-effector away from the person's hand [3]. That's a concrete safety metric you can implement: measure the distance between the robot's planned grasp and the human's hand, and reject any grasp that comes too close.
Real-world testing should also include objects with uncertain shapes. A 2025 study on tactile exploration showed that a proactive strategy—choosing where to touch next to minimize uncertainty—reduces the risk of contact failure when the estimated surface differs from the real object [1]. In practice, that means before a robot grasps an unfamiliar object near a person, it should be allowed to explore the surface with touch sensors, not just rely on vision.
The final step: controlled trials with people, measuring safety and success
The last stage is a controlled human-robot interaction trial, where you measure both success rate and safety. Fast-EgoHOS was validated in a real robotic setup across various object types and grip configurations, achieving performance comparable to larger models while running three times faster—that speed matters in close-contact scenarios because it reduces the time a person is exposed to a moving robot [3].
Human-inspired grasp planning can also inform what to test. A 2022 study on agricultural robots used a tactile glove to study human grasping, then designed pre-gripping plans based on that data [4]. For object-agnostic planning, that suggests you should test not just whether the grasp succeeds, but whether the grasp is stable and safe under different object shapes—using human grasp patterns as a benchmark.
About These Sources
This answer is built on 5 peer-reviewed studies — published from 2022 to 2025, 3 from 2024 or later, 1 in Q1–Q2 journals, collectively cited 91 times — selected as the most relevant from 5 studies that passed quality screening, drawn from 64 papers retrieved from a database of over 500 million.
Sources used in this answer
Proactive Tactile Exploration for Object-Agnostic Shape Reconstruction from Minimal Visual Priors
Proposed an iterative tactile exploration method that reduces contact failure risk by minimizing uncertainty with the least number of contacts, evaluated in both simulation and real setup.
DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation
Generated 1.32 million grasps for 5,355 objects using ShadowHand, all validated in Isaac Gym simulator, showing higher diversity and quality than previous datasets.
Fast-EgoHOS: An Efficient Framework based on Hand-Object Segmentation for Object-Agnostic Human-to-Robot Handovers
Validated an object-agnostic handover framework in real robotic setups across various objects, achieving comparable performance to larger models while reducing inference speed to one third, with safety criteria like hand-grasp distance.
Human Grasp Mechanism Understanding, Human-Inspired Grasp Control and Robotic Grasping Planning for Agricultural Robots
Used a multi-sensor tactile glove to study human grasping, summarizing a grasp gesture library and setting sliding detection thresholds to ensure robust grasping, informing pre-gripping planning.
SKT-Hang: Hanging Everyday Objects via Object-Agnostic Semantic Keypoint Trajectory Generation
Proposed Semantic Keypoint Trajectory (SKT) for object-agnostic hanging, trained in simulation and showing promising real-world results, with substantial improvements over existing methods.